The classical face recognition approach is to store an image of a person's face, and then to provide a face matching algorithm robust enough to handle varying lighting conditions, facial expressions, face directions, glasses, beard, mustache and facial hair, etc.
In the area of face recognition technology, research has focused almost entirely on developing algorithms that are invariant to the lighting conditions, the facial expressions and the face direction. Such systems obtain simple databases at the expense of complex matching algorithms or family of algorithms. Alternatively, the face recognition systems based on face template matching and neural networks, require a large number of face samples to train the network to an acceptable standard. The operations that are applied to train neural networks are mainly of linear geometric nature, such as scaling or zooming.
The problems of these techniques is their weakness in dealing with various lighting conditions, changes of expression as well as time difference between the registration and the time of the observation.
For example, WO 99/53427 provides a technique for detecting and recognizing an object in an image frame. The object identification and recognition process uses an image processing technique based on model graphs and bunch graphs that efficiently represent image features as jets. The jets are composed of wavelet transforms and are processed at nodes or landmark locations on an image corresponding to readily identifiable features. The authors thus propose a face representation based on a set of feature points extracted from the face images. Face recognition is then realised using a deformable graph matching technique.
WO 98/37507 discloses that to automatically recognize the face of a person in real time or on the basis of a photo document, a digital image of a side view of the head is produced and a profile curve determined by linear extraction. From the profile curve a strongly reduced quantity of data is generated by evaluation algorithms to serve as a model of the person concerned. This allows for especially simple biometric face identification requiring minimum memory space for the model code.
In general, attempts to improve face recognition have resulted in various methods and algorithms to extract features and compare features with data stored on a database. However, various conditions and circumstances can still lead to less desirable results, and thus an alternative approach is required to improve face recognition.